Scoped Prompting vs Open Prompting: A Comparative Analysis
How to choose the best prompting strategy for your natural language generation system
Natural language generation (NLG) is the task of producing natural language text from non-linguistic data, such as images, tables, graphs, or structured information. NLG systems can be used for various purposes, such as summarization, translation, dialogue, storytelling, and content creation. However, designing an effective NLG system is not a trivial task, as it requires a careful balance between the quality, diversity, and relevance of the generated text.
One of the key challenges in NLG is how to prompt the system to generate the desired text. A prompt is a piece of text that serves as an input or a trigger for the NLG system, and it can influence the style, tone, content, and structure of the output. Prompts can vary in their level of specificity, detail, and guidance, and different prompting strategies can have different effects on the performance and behavior of the NLG system.
In this article, I’ll talk about how to compare and contrast two main types of prompting strategies: scoped prompting and open prompting. Scoped prompting is a technique that provides a clear and explicit specification of what the system should generate, such as a template, a question, or a set of keywords. Open prompting is a technique that provides a vague or implicit specification of what the system should generate, such as a topic, a genre, or a mood. In this article I’ll discuss the advantages and disadvantages of each approach and provide some examples of how they can be used in different domains and tasks.
Scoped Prompting
Scoped prompting is a prompting strategy that aims to reduce the ambiguity and uncertainty of the NLG task by providing a clear and explicit specification of what the system should generate. Scoped prompting can take various forms, such as:
Templates: A template is a predefined structure or format that the system should follow to generate the text, such as a headline, a summary, or a paragraph. A template can specify the number of words, sentences, or paragraphs, the order and arrangement of the information, and the linguistic features, such as tense, voice, or modality. For example, a template for a Copilot for Security in Defender summary could be: "[Who] [did what] [where] [when] [why] [how]."
Questions: A question is a direct and specific request that the system should answer in natural language, such as "Who is the owner of the Intune enrolled device?" or "How to create a Conditional Access policy?". A question can specify the type and scope of the answer, such as yes/no, factual, opinion, or procedural, and the level of detail, such as brief or elaborate.
Keywords: A keyword is a word or a phrase that represents the main idea or the theme of the text that the system should generate, such as "threat intelligence" or "zero-day CVE-2024-30080". A keyword can specify the topic and the domain of the text, but not the style, tone, or structure.
The main advantage of scoped prompting is that it can increase the quality and relevance of the generated text, as it reduces the search space and the complexity of the NLG task and guides the system to produce the expected output. Scoped prompting can also increase the efficiency and the speed of the generation process, as it reduces the need for trial and error and feedback loops. Scoped prompting can be especially useful for tasks that require high accuracy, consistency, and reliability, such as summarization, translation, or information extraction.
The main disadvantage of scoped prompting is that it can limit the diversity and creativity of the generated text, as it constrains the system to follow a fixed or a predetermined pattern and discourages the system from exploring alternative or novel ways of expressing the information. Scoped prompting can also increase the dependency and the rigidity of the system, as it requires a well-defined and a well-formed prompt, and may fail or produce errors if the prompt is incomplete, incorrect, or inconsistent. Scoped prompting can be less suitable for tasks that require high flexibility, adaptability, and originality, such as dialogue, storytelling, or content creation.
Open Prompting
Open prompting is a prompting strategy that aims to increase the diversity and creativity of the NLG task by providing a vague or implicit specification of what the system should generate. Open prompting can take various forms, such as:
Topics: A topic is a word or a phrase that represents the general subject or the area of interest of the text that the system should generate, such as "cybersecurity" or "threat". A topic can specify the domain and the scope of the text, but not the content, the style, or the structure.
Genres: A genre is a word or a phrase that represents the type or the category of the text that the system should generate, such as "summary" or "review". A genre can specify the purpose and the audience of the text, but not the topic, the tone, or the format.
Moods: A mood is a word or a phrase that represents the emotion or the attitude of the text that the system should generate, such as "happy" or "angry". A mood can specify the tone and the sentiment of the text, but not the topic, the genre, or the structure.
The main advantage of open prompting is that it can increase the diversity and creativity of the generated text, as it allows the system to explore the search space and the complexity of the NLG task and encourages the system to produce unexpected or novel output. Open prompting can also increase the autonomy and the flexibility of the system, as it does not require a well-defined or a well-formed prompt, and can handle incomplete, incorrect, or inconsistent input. Open prompting can be especially useful for tasks that require high variability, novelty, and originality, such as dialogue, storytelling, or content creation.
The main disadvantage of open prompting is that it can decrease the quality and relevance of the generated text, as it increases the ambiguity and uncertainty of the NLG task and does not guide the system to produce the desired output. Open prompting can also decrease the efficiency and the speed of the generation process, as it increases the need for trial and error and feedback loops. Open prompting can be less suitable for tasks that require high accuracy, consistency, and reliability, such as summarization, translation, or information extraction.
TLDR
The comparative analysis of scoped and open prompting strategies reveals that while scoped prompting ensures higher quality and relevance in text generation by providing explicit instructions, it may hinder the creative and diverse expression of ideas. Open prompting encourages a broader exploration of ideas and creative outputs, albeit at the potential cost of precision and relevance. Ultimately, the choice between scoped and open prompting should be guided by the specific requirements of the natural language generation task at hand, balancing the need for accuracy and innovation to achieve the most effective communication.
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